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Modeling Dynamic Interference for Treatment Effect Estimation from Dynamic Graphs
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AI
DOI：10.1145/3796240.png)
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Abstract
En 中文
Estimating treatment effects can assist decision-making in various areas, such as commerce and medicine. One application of the treatment effect estimation is to predict the effect of an advertisement on the purchase result of a customer, known as individual treatment effect (ITE). In online websites, the outcome of an individual can be affected by treatments of other individuals, as people often propagate information with their friends. This is referred to as interference. Prior studies have attempted to model interference for accurate ITE estimation under a static network among individuals. However, the network usually changes over time in real-world applications due to complex social activities among individuals. In this case, the outcomes of individuals can be interfered with not only by treatments for current neighbors but also by past information and treatments for past neighbors, which we refer to as dynamic interference. In this work, we model dynamic interference by developing an architecture to aggregate both the past information of individuals and their neighbors. Specifically, our proposed method contains an attention-based historical aggregation, which models interference received by individuals from previous timestamps, and an attention-based neighbor aggregation, which captures interference received by individuals within every timestamp. Since information about individuals changes over time, we propose a parameter evolution trick to adaptively update the parameters of the model, which enables the model to capture the dynamics effectively. In our experiments on multiple datasets with dynamic interference, our method outperforms existing methods for ITE estimation because they cannot capture dynamic interference, which corroborates the importance of dynamic interference modeling.
Keywords:
Causal Inference
Treatment Effect Estimation
Interference
Dynamic Graph
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